Estimation of background noise and its effect on sonar range estimation

ABSTRACT

A system and method for estimating the SNR in a sonar environment and for determining the effect of the estimated SNR on sonar ranging accuracy. The system includes a sensor, a transmitter, a receiver, a plurality of band-pass filters, a cross correlator, and a data analyzer. The transmitter transmits a first signal having a predetermined frequency range through a transmission medium. The sensor generates a second signal corresponding to an echo signal reflected from an object. The first and second signals are provided to the band-pass filters, each operative to pass a respective sub-band of frequencies. The filters provide filtered versions of the first and second signals to the cross correlator, which performs cross correlation operations on the filtered signals. A data analyzer analyzes the cross correlator output data to determine the variability of cross correlation peaks within each frequency sub-band, thereby allowing more accurate SNR estimations in noisy environments.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority of U.S. Provisional Patent ApplicationNo. 60/476,847 filed Jun. 9, 2003 entitled ESTIMATION OF BACKGROUNDNOISE AND ITS EFFECT ON SONAR RANGE ESTIMATION.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

The present invention was made with Government support under U.S.Government Contract Nos. ARO DAAD 19-02-1-0403 and ONRN00012-02-C-02960.

BACKGROUND OF THE INVENTION

The present application relates generally to sonar systems, and morespecifically to systems and methods of determining the effect ofbackground noise on sonar range estimation.

Sonar systems are known that employ sonar pulses reflected from anobject or target to estimate a distance to the target (also known asestimating the range of the target). A conventional system forperforming sonar range estimation is typically configured to transmitone or more sonar pulses comprising sonic or supersonic pressure wavestoward a selected target, and to receive one or more sonar pulsesreflected from the target. Such reflected sonar pulses, which arecommonly called echoes or returns, may include a significant amount ofbackground noise and/or other interfering signals in addition to areflected sonar signal of interest. The conventional sonar systemtypically includes a coherent receiver (also known as a matched filterreceiver) configured to receive both the echo and a representation ofthe transmitted sonar pulse. For example, the coherent receiver maycomprise a cross correlator. The echo and the representation of thetransmitted sonar pulse are cross-correlated within the coherentreceiver to generate a peak cross correlation value, which is comparedto a predetermined threshold value. If the cross correlation value isgreater than the predetermined threshold value, then the reflected sonarsignal of interest has been successfully detected. The conventionalsonar system then utilizes the cross correlation peak to obtain ameasure of the range of the target.

One drawback of the above-described conventional sonar system is thatthe level of background noise and/or other interfering signals containedwithin the echo or return may be sufficient to cause the reflected sonarsignal to go undetected or to be falsely detected, thereby causing thecross correlator to produce inaccurate range measurements. Suchinaccurate range measurements are likely to occur in low signal-to-noiseratio (SNR) sonar environments, in which the noise power within the echomay be comparable to or greater than the reflected signal power. Thiscan be problematic in sonar range estimation systems because a reductionin the measurement accuracy of the cross correlator typically leads to aconcomitant reduction in sonar range accuracy.

Prior attempts to increase the accuracy of sonar range measurements innoisy sonar environments have included filtering out at least some ofthe background noise before providing the echo to the cross correlator.However, such attempts have generally not worked well enough to allowsuccessful detection of reflected sonar signals and accurate estimationof range in low SNR sonar environments. This is due, at least in part,to the fact that sonar systems typically receive sonar pulses thatinclude various types of noise from a variety of different noisesources. For example, a sonar system may transmit sonar pulses through amedium such as water from a ship or submarine that produces noise acrossa wide range of frequency. Further, other ships, submarines, orstructures producing noise across wide frequency ranges may be withinthe vicinity of the sonar system. Moreover, the natural interaction ofthe water and objects within the water including the selected target mayproduce a substantial amount of ambient noise.

In addition, sonar ranging systems may receive echoes from a pluralityof selected (and unselected) targets, each target having its ownassociated noise level, and it may be desirable to determine the noiselevel and range of each target separately. Such noise associated withmultiple targets may be stationary or non-stationary, linear ornonlinear, or additive or non-additive. Further, at least some of thebackground noise may result from reverberations and/or random signaldistortions of the transmitted or reflected sonar pulse, and thereforeboth the noise level and its structure may be significantly affected bythe transmitted sonar signal. However, conventional sonar systems aregenerally incapable of accurately estimating noise levels and targetranges in the presence of non-stationary, nonlinear, non-additive,and/or signal-dependent noise.

Moreover, the density and temperature of the transmission medium (e.g.,water) and the frequency of the transmitted/reflected sonar signal mayaffect the decay rate of the sonar pulse propagating through the medium.In addition, the absorption of certain frequencies of the transmittedsonar pulse by the target may affect the strength of the resulting echoor return. However, conventional sonar systems are generally incapableof fully compensating for such factors when called upon to generateaccurate noise and range estimates.

It would therefore be desirable to have a system and method ofdetermining the effects of background noise on sonar range estimation.Such a system would be capable of estimating background noise effectswhether the noise is stationary or non-stationary, linear or non-linear,additive or non-additive, or signal-dependent or non-signal-dependent.It would also be desirable to have a method of estimating backgroundnoise effects that can be used to increase the accuracy of sonar rangeestimation in low SNR sonar environments.

BRIEF SUMMARY OF THE INVENTION

In accordance with the present invention, a system and method isprovided for estimating the signal-to-noise ratio (SNR) in a noisy sonarenvironment and for determining the effect of the estimated SNR on sonarranging accuracy. Benefits of the presently disclosed system and methodare achieved by performing multiple cross correlation operations usingat least one transmitted sonar pulse and at least one reflected sonarpulse to determine the variability of multiple cross correlation peaks,in which the multiple cross correlation peaks correspond to respectivefrequency sub-bands spanning the frequency range of the transmittedsignal. By determining the lowest frequency sub-band having acorresponding low peak ambiguity, the SNR within a sonar environment canbe accurately estimated, thereby allowing more accurate sonar rangeestimations.

In one embodiment, a system for estimating the SNR in an environmentcomprises a sensor, a transmitter, a receiver, a plurality of band-passfilters, a cross correlator, and a data analyzer. The transmitter isconfigured to transmit at least one pulse through a transmission mediumsuch as air, water, soil, or living tissue. The transmitted pulsetravels through the transmission medium until it strikes an object,which returns at least one reflected pulse (echo or return) to thesensor. The sensor is configured to provide a signal representative ofthe echo to the receiver, which subsequently provides an indication ofthe echo to the plurality of band-pass filters. Each band-pass filter isconfigured to pass a respective sub-band of frequencies, in which therespective frequency sub-bands substantially span the frequency range ofthe transmitted pulse. The echo and a representation of the transmittedpulse are filtered by the respective band-pass filters, which providefiltered versions of the echo and pulse to the cross correlator. Thecross correlator is configured to perform multiple cross correlationoperations, in which each cross correlation operation operates onfiltered versions of the echo and pulse produced by a respective one ofthe band-pass filters. The cross correlator provides cross correlationoutput data to the data analyzer, which is operative to analyze the datato determine the variability of cross correlation peaks within eachfrequency sub-band, and to identify the lowest frequency sub-band havinga corresponding low peak ambiguity. By referencing the peak variabilityversus SNR corresponding to the identified frequency sub-band, which maybe theoretically or empirically determined, the data analyzer is furtheroperative to provide an accurate estimation of the SNR within theenvironment of interest.

By analyzing the output data of multiple cross correlation operationsperformed on at least one filtered transmitted pulse and at least onefiltered reflected pulse, the presently disclosed system and method candetermine peak variability within multiple frequency sub-bands, therebyallowing more accurate SNR estimations in noisy environments.

The disclosed system and method provides an estimation of theinstantaneous SNR of a signal (e.g., a sonar pulse or “ping”).Specifically, if the ping strikes several layers inside the object, thereturn signal from the inner layers is weaker and therefore the SNR isreduced. Due to reverberations inside the object, the SNR may be furtherreduced. The presently disclosed system and method estimate the SNR foreach returning ping separately, and therefore adapt to the correctbandwidth for each returning ping.

Other features, functions, and aspects of the invention will be evidentfrom the Detailed Description of the Invention that follows.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention will be more fully understood with reference to thefollowing Detailed Description of the Invention in conjunction with thedrawings of which:

FIG. 1 is a block diagram of a system for estimating the signal-to-noiseratio in an environment according to the present invention;

FIGS. 2 a-2 b are diagrams of ambiguity functions illustrating theeffect of noise level on the variability of cross correlation peaks;

FIG. 3 a is a diagram illustrating peak variability as a function ofsignal-to-noise ratio and center frequency for a plurality of frequencysub-bands;

FIG. 3 b is a diagram illustrating a performance curve derived from thediagram of FIG. 3 b;

FIGS. 4 a-4 c are diagrams illustrating empirical estimations of sonarranging accuracy; and

FIGS. 5 a-5 b are flow diagrams illustrating methods of operating thesystem of FIG. 1.

DETAILED DESCRIPTION OF THE INVENTION

U.S. Provisional Patent Application No. 60/476,847 filed Jun. 9, 2003entitled ESTIMATION OF BACKGROUND NOISE AND ITS EFFECT ON SONAR RANGEESTIMATION is incorporated herein by reference.

Systems and methods of estimating the signal-to-noise ratio (SNR) in anoisy environment are disclosed that may be used to increase theaccuracy of sonar range estimation in low SNR sonar environments. Thepresently disclosed systems and methods obtain such SNR estimations viaa determination of the variability of multiple cross correlation peakscorresponding to a plurality of frequency sub-bands spanning thefrequency range of at least one transmitted pulse.

FIG. 1 depicts an illustrative embodiment of a system 100 for estimatingthe SNR in an environment, in accordance with the present invention. Inthe illustrated embodiment, the system 100 comprises a sensor 102, atransmitter 103, a receiver 104, a plurality of band-pass filters 106, across correlator 108, and a data analyzer 110. It is noted that theillustrative embodiment of the system 100 described herein is suitablefor estimating the SNR in a sonar environment. For example, the sonarsystem 100 may be adapted for (1) marine exploration in an underwaterenvironment, (2) seismic exploration in a soil environment, (3) medicalultrasound in an environment comprising living tissue, or any othersuitable use in a sonar environment. It should be understood, however,that the presently disclosed system 100 for estimating SNR may also beadapted for use in radar systems, microwave systems, laser systems, orany other suitable system.

Specifically, the sonar system 100 includes the sonar transmitter 103,which is configured to transmit at least one sonar pulse through atransmission medium such as water. The transmitted sonar pulse travelsthrough the water until it strikes an object or target 112 in the water,which returns at least one reflected sonar pulse (commonly known as anecho or return) toward the sonar sensor 102. For example, the sonarsensor 102 may comprise one or more hydrophone sensors. The sensor 102is configured to sense the echo, and to provide a signal representativeof the echo to the sonar receiver 104, which in turn provides anindication of the echo to the plurality of band-pass filters 106.

In the illustrated embodiment, each of the band-pass filters 106.1-106.nis configured to pass a respective sub-band of frequencies, in which therespective frequency sub-bands are defined to span the frequency rangeof the sonar pulse transmitted by the sonar transmitter 103. Forexample, in the event the frequency range of the transmitted sonar pulseis about 87 kHz, the respective frequency sub-bands may approximatelyrange from 6-18 kHz, 18-31 kHz, 31-43 kHz, 43-56 kHz, 56-68 kHz, 68-81kHz, and 81-93 kHz, thereby spanning the pulse frequency range of 87kHz. Accordingly, in this illustrative example, the center frequency ofthe frequency sub-bands may be approximately equal to 12 kHz, 25 kHz, 37kHz, 50 kHz, 62 kHz, 75 kHz, and 87 kHz, respectively.

As shown in FIG. 1, the plurality of band-pass filters 106 is configuredto receive the echo indication from the sonar receiver 104, and toreceive a representation of the sonar pulse transmitted by the sonartransmitter 103. The echo and the representation of the transmittedpulse are filtered by each of the respective band-pass filters106.1-106.n, which subsequently provide band-pass filtered versions ofthe echo and the transmitted pulse to the cross correlator 108. Thecross correlator 108 is configured to perform multiple cross correlationoperations on the filtered echoes and pulses. Specifically, the crosscorrelator 108 cross-correlates the filtered versions of the echoes andpulses provided by each of the band-pass filters 106.1-106.n, andprovides corresponding cross correlation output data to the dataanalyzer 110, which is operative to analyze the data to determine thevariability of cross correlation peaks within each frequency sub-band,and to identify the lowest frequency sub-band having a corresponding lowpeak ambiguity. The operation of the data analyzer within the presentlydisclosed system is described in greater detail below.

The cross correlation of an echo and a pulse may be expressed as

$\begin{matrix}\begin{matrix}{{\psi_{e} \cdot {\psi_{p}(\tau)}} = \left. {\int{{\psi_{e}(t)}\mspace{11mu}{\psi_{p}\left( {t + \tau} \right)}\mspace{11mu}{\mathbb{d}t}}}\Rightarrow \right.} \\{{= {{\int{{\psi_{p}(t)}\mspace{11mu}{\psi_{p}\left( {t + \tau + \tau_{0}} \right)}\mspace{11mu}{\mathbb{d}t}}} + {\int{{\psi_{p}(t)}\mspace{11mu}\eta\mspace{11mu}\left( {t + \tau} \right)\mspace{11mu}{\mathbb{d}t}}}}},}\end{matrix} & (1)\end{matrix}$in which the first term “∫ψ_(p)(t)ψ_(p)(t+τ+τ₀)dt” is theauto-correlation of the pulse centered at time τ₀, the second term“∫ψ_(p)(t)η(t+τ)dt” is representative of band-limited white noise withfrequency limits defined by the spectrum of the pulse, and theintegration operation in each term is performed from −∞ to +∞. FIGS. 2a-2 b depict representative ambiguity functions 201-203 that may beemployed to describe the output provided by the cross correlator 108(see FIG. 1). Because the cross correlator 108 cross correlates thefiltered echo and pulse provided by each of the band-pass filters106.1-106.n, each of which passes a respective sub-band of frequencies,it is understood that an ambiguity function may be constructedcorresponding to each of the frequency sub-bands.

As shown in FIGS. 2 a-2 b, the ambiguity functions 201-203 are expressedas functions of pulse amplitude (vertical axis, dB) and delay time(horizontal axis, seconds), which is proportional to range.Specifically, the ambiguity functions 201-203 correspond to the crosscorrelation of respective echo and pulse pairs having approximately thesame frequency range but different center frequency fc (i.e., meanintegrated frequency). For example, the ambiguity function 201corresponds to the cross correlation of a first echo and pulse pairhaving a low center frequency fc1, the ambiguity function 202corresponds to the cross correlation of a second echo and pulse pairhaving an intermediate center frequency fc2, and the ambiguity function203 corresponds to the cross correlation of a third echo and pulse pairhaving a high center frequency fc3. FIG. 2 a depicts a detailed view ofthe main lobes of the ambiguity functions 201-203, and FIG. 2 b depictsthe main lobes and the side lobes of the ambiguity functions 201-203.Each one of the ambiguity functions 201-203 comprises a respective peakvalue, which is indicative of the range of the object or targetreturning the echo.

In high SNR sonar environments (i.e., when the noise level is low), thepeak of the ambiguity function is generally located at the main lobe ofthe function. In this case, the peaks of the ambiguity functions 201-203are regarded as having low ambiguity, and may be located within thewidth of the main lobes of the functions at about time τ₀, asillustrated by the vertical line of FIG. 2 a. It is appreciated that thetime τ₀ corresponds to the actual range of the target. The effect of thelow level of noise in such high SNR sonar environments is to jitter theposition of the peak around the time τ₀. To a first approximation, themagnitude of this jitter (also known as peak variability) is relativelylow, e.g., the peak variability is typically less than the width of themain lobes, as illustrated by the horizontal lines of FIG. 2 a. Thelengths of the horizontal lines of FIG. 2 a are indicative of the levelsof peak variability associated with the respective ambiguity functions201-203. In the illustrated embodiment, the lowest peak variability isassociated with the ambiguity function 203 (high center frequency fc3),and the highest peak variability is associated with the ambiguityfunction 201 (low center frequency fc1).

In low SNR sonar environments (i.e., when the noise level is high, forexample, when the noise level is of the order of the difference betweenthe amplitudes of the main lobe and the first side lobe), the peak ofthe ambiguity function may not be located within the main lobe of thefunction, but instead may be located at one of the side lobes. In thiscase, the peaks of the ambiguity functions 201-203 are regarded ashaving high ambiguity, and may be located (1) within the width of a sidelobe at about time τ⁻² for function 203, (2) within the width of a sidelobe at about time τ_(−2.5) for function 202, and (3) within the widthof a side lobe at about time τ⁻³ for function 201, as illustrated by thevertical lines 203 a, 202 a, and 201 a, respectively, of FIG. 2 b. Theeffect of the high level of noise in such low SNR sonar environments isto significantly increase the peak variability, thereby increasing thepotential error in sonar range estimation. The horizontal lines in FIG.2 b illustrate the potential error in range estimation that can resultfrom such high noise levels.

FIG. 3 a depicts peak variability as a function of SNR (dB) and centerfrequency fc for a plurality of frequency sub-bands. In thisillustrative example, peak variability is expressed in terms of rootmean square error (RMSE, seconds), which is a temporal representation ofthe potential error in range estimation. Further, the center frequenciesfc of the frequency sub-bands are equal to 12 kHz, 25 kHz, 37 kHz, 50kHz, 62 kHz, 75 kHz, and 87 kHz, respectively, and the centralized rootmean square bandwidth B_(CRMS) of transmitted pulses is fixed at 2.1kHz.

For example, peak variability curves 301-307, as depicted in FIG. 3 a,may be obtained via Monte Carlo simulations. Specifically, thetransmitted pulses may be expressed as cosine packets of the formψ_(σ,η)(t)=K _(σ,η) exp(−t ²/2σ²)cos(2πηt),  (2)in which “η” is the center frequency; “σ” is the standard deviation of apeak location in time, which controls the spread in time of the pulseand its frequency bandwidth; and, “K_(σ,η)” is a normalization factorsuch that∫ψ² _(σ,η)(t)dt=1,  (3)in which the integration operation is performed from −∞to +∞. Further,white noise may be added to the pulse to generate a noisy echo for thesimulation, and a temporal indication of the range estimate may becomputed as the time corresponding to the maximum amplitude of the crosscorrelation between the echo and pulse.

As shown in FIG. 3 a, each of the simulation curves 301-307 isapproximately linear within a first SNR range of about 35-50 dB (seealso region I of FIG. 3 b). Further, for each curve 301-307, there is asharp transition from lower RMSE levels to higher RMSE levels within asecond SNR range of about 15-35 dB (see also region II of FIG. 3 b),thereby indicating significant increases in peak variability. Within athird SNR range of about 5-15 dB (see also region III of FIG. 3 b), thecurves 301-307 are again approximately linear. It is noted that thecurve 308 depicted in FIG. 3 b is a performance curve comprising apartial composite of the peak variability curves 301-307, includingbreak points 1-9 (see FIG. 3 a). Accordingly, as the SNR decreases(i.e., as the noise level increases), the corresponding RMSE valuesgradually increase within region I until sharp transitions occur fromlower RMSE levels to significantly higher RMSE levels within regionII—the RMSE values then continue to increase more rapidly at the higherRMSE levels within region III. It is noted that within region III, thesonar range resolution falls sharply to a point where the sonar isineffective and the target is considered to be out-of-range.

Specifically, within region I, the simulation curves 301-307approximately track a line 310 (see FIG. 3 b), which may be defined asσ=(2πB _(RMS) d)⁻¹,  (4)in which “σ” is the standard deviation of a peak location in time and isproportional to the RMSE, “B_(RMS)” is the root mean square bandwidth ofthe pulse, and “d” is the SNR. A derivation of equation (4) is describedin Probability and Information Theory with Applications to Radar, P. M.Woodward, New York, McGraw-Hill Book Company, Inc., copyright 1953,which is incorporated herein by reference. It is noted that B_(RMS) maybe expressed asB _(RMS)=(∫f ² P _(SD)(f)df)^(1/2),  (5)in which “P_(SD)(f)” is the power spectral density of the pulse, and theintegration operation is performed from 0 to +∞. Further, d may beexpressed asd=(2E/N ₀)^(1/2),  (6)in which “E” is the total energy of the echo, and “N₀” is the spectraldensity of the noise. Accordingly,SNR(dB)=20 log₁₀ d.  (7)

Moreover, following the sharp transitions from lower RMSE levels tohigher RMSE levels within region II (see FIG. 3 b), the simulationcurves 301-307 approximately track a line 312 (see FIG. 3 b), which maybe defined asσ=(2πB _(CRMS) d)⁻¹,  (8)in which “B_(CRMS)” is the centralized root mean square bandwidth of thetransmitted pulse. The RMSE values continue to increase at a faster ratewithin region III (see FIG. 3 b). It is noted that B_(CRMS) may beexpressed asB _(CRMS)=(∫(f−fc)² P _(SD)(f)df)^(1/2),  (9)in which “fc” is the center frequency of the pulse, and the integrationoperation is performed from 0 to +∞. It is further noted that fc may beexpressed asfc=∫fP _(SD)(f)df,  (10)in which the integration operation is performed from 0 to +∞. Moreover,the root mean square bandwidth may be expressed asB _(RMS) ² =B _(CRMS) ² +fc ².  (11)Accordingly, in the event the center frequency fc is much larger thanthe centralized root mean square bandwidth B_(CRMS),B_(RMS)≈fc.  (12)

The behavior of the simulation curves 301-307 within region I (see FIGS.3 a-3 b) is characteristic of the performance of a “coherent” receiver,which estimates the range of a target relative to a peak of theambiguity function within the width of the function's main lobe. Thebehavior of the curves 301-307 after their sharp transitions from lowerRMSE levels to higher RMSE levels within region II (see FIGS. 3 a-3 b)is characteristic of the performance of a “semi-coherent” receiver,which estimates target range relative to the peak of the envelope of theambiguity function. As illustrated in FIG. 3 a, range estimates providedby the semi-coherent receiver have associated errors (RMSE) that aresignificantly higher than the errors associated with the range estimatesof the coherent receiver.

The embodiments disclosed herein will be better understood withreference to the following illustrative examples and FIG. 1. In a firstexample, the sonar transmitter 103 transmits a single sonar pulse(“ping”) having a frequency range of about 87 kHz through a transmissionmedium such as water. The transmitted ping travels through the wateruntil it strikes the selected target 112, which returns a reflectedsonar pulse (“echo”) to the sonar sensor 102. It is understood that atypical target may return multiple echoes. The target 112 in thisexample is described as returning a single echo for clarity ofillustration. The sensor 102 provides a signal representative of theecho to the sonar receiver 104, which subsequently provides anindication of the echo to the plurality of band-pass filters 106.

In this illustrative example, the plurality of band-pass filters106.1-106.n passes respective frequency sub-bands ranging from 6-18 kHz,18-31 kHz, 31-43 kHz, 43-56 kHz, 56-68 kHz, 68-81 kHz, and 81-93 kHz,thereby spanning the ping frequency range of 87 kHz. The centerfrequencies fc of the frequency sub-bands are therefore approximatelyequal to 12 kHz, 25 kHz, 37 kHz, 50 kHz, 62 kHz, 75 kHz, and 87 kHz,respectively. Further, the centralized root mean square bandwidthB_(CRMS) of the transmitted pulse is fixed at about 2.1 kHz.

Next, the echo and a representation of the transmitted ping are filteredby each of the band-pass filters 106.1-106.n, which provide filteredversions of the echo and ping to the cross correlator 108. The crosscorrelator 108 then performs multiple cross correlation operations onthe filtered versions of the echo and ping, and provides multiple setsof cross correlation output data corresponding to the respectivefrequency sub-bands to the data analyzer 110.

As described above, the data analyzer 110 of the presently disclosedembodiment is operative to analyze the cross correlation output data todetermine the variability of cross correlation peaks within eachfrequency sub-band, and to identify the lowest frequency sub-band havinga corresponding low peak ambiguity. To this end, the data analyzer 110effectively constructs an ambiguity function like the ambiguityfunctions 201-203 (see FIGS. 2 a-2 b) for each of the frequencysub-bands 6-18 kHz, 18-31 kHz, 31-43 kHz, 43-56 kHz, 56-68 kHz, 68-81kHz, and 81-93 kHz, and determines the peak variability for eachfrequency sub-band using the ambiguity functions.

In this illustrative example, it is assumed that an analysis of thecross correlation output data by the data analyzer 110 determines thatthe peak ambiguity corresponding to each of the frequency sub-bands56-68 kHz, 68-81 kHz, and 81-93 kHz is low (i.e., the peak variabilityis within the width of the main lobe in each of the correspondingambiguity functions), and the peak ambiguity corresponding to each ofthe frequency sub-bands 6-18 kHz, 18-31 kHz, 31-43 kHz, 43-56 kHz ishigh (i.e., the peak variability varies between the side lobes in eachof the associated ambiguity functions). In this example, the operationof the sonar system 100 is therefore like that of a coherent receiverwhen transmitting pings having frequencies ranging from 56-93 kHz, and asemi-coherent receiver when transmitting pings having frequenciesranging from 6-56 kHz.

Accordingly, the data analyzer 110 identifies the frequency sub-band56-68 kHz as the lowest frequency sub-band having a corresponding lowpeak ambiguity. Next, the data analyzer 110 analyzes the peakvariability data versus SNR for the identified sub-band 56-68 kHz, asdepicted in FIG. 3 a by the curve 305, and determines the break point onthe curve where the sharp transition from low RMSE levels to high RMSElevels begins, i.e., break point 7. It is noted that break point 7 onthe peak variability versus SNR curve 305 corresponds to a RMSE of about10⁻⁷ and an SNR of about 30 dB. The data analyzer 110 thereforeestimates the SNR within the sonar environment to be about 30 dB forechoes and pings with a center frequency of 62 kHz and a centralizedroot mean square bandwidth of 2.1 kHz.

It should be understood that instead of transmitting a single pinghaving a frequency range of about 87 kHz and band-pass filtering theresulting echo, as in the first example above, a single pingcorresponding to each of the frequency sub-bands 6-18 kHz, 18-31 kHz,31-43 kHz, 43-56 kHz, 56-68 kHz, 68-81 kHz, and 81-93 kHz mayalternatively be transmitted. Next, multiple cross correlationoperations may be performed on the echoes and pings corresponding toeach frequency sub-band, and multiple sets of cross correlation outputdata corresponding to the respective frequency sub-bands may be providedto the data analyzer, which generates results essentially the same asthose generated in the first example above, i.e., a single distributionis formed by one estimate per frequency band. In both cases, the centerfrequency fc may be determined such that the overall distribution ofcross correlation output data is the combination of two distributionshaving different standard deviations, i.e., a first distributioncorresponding to estimates above the center frequency and a seconddistribution corresponding to estimates below the center frequency, inwhich the second distribution has a standard deviation smaller than thefirst distribution. The determined center frequency fc corresponds tothe breakpoint of interest, e.g., break point 7 in the first exampleabove (see FIG. 3 a). It is also understood that the data analysisresults obtained from transmitting the single broadband ping and thoseobtained from transmitting the multiple narrow-band pings may becombined to determine the center frequency fc corresponding to thebreakpoint of interest.

In a second example, instead of transmitting the single broadband pinghaving a frequency range of about 87 kHz, the sonar transmitter 103transmits multiple broadband pings through the water. It is understoodthat the centralized root mean square bandwidth B_(CRMS) of the multiplebroadband pings is fixed at about 2.1 kHz. As in the first exampledescribed above, the multiple pings travel through the water until theystrike one or more selected targets 112, which return reflected sonarpulses (echoes) to the sonar sensor 102. The sensor 102 in turn providessignals representative of the echoes to the sonar receiver 104, whichsubsequently provides indications of the echoes to the plurality ofband-pass filters 106.

As in the first example above, the plurality of band-pass filters106.1-106.n passes respective frequency sub-bands ranging from 6-18 kHz,18-31 kHz, 31-43 kHz, 43-56 kHz, 56-68 kHz, 68-81 kHz, and 81-93 kHz.The center frequencies of the respective frequency sub-bands aretherefore approximately equal to 12 kHz, 25 kHz, 37 kHz, 50 kHz, 62 kHz,75 kHz, and 87 kHz. Accordingly, the sonar receiver 104 provides themultiple broadband pings to the band-pass filters 106.1-106.n passingsub-bands 6-18 kHz, 18-31 kHz, 31-43 kHz, 43-56 kHz, 56-68 kHz, 68-81kHz, and 81-93 kHz, respectively.

The echoes and representations of the corresponding transmitted pingsare then filtered by the band-pass filters 106.1-106.n, which providefiltered versions of the echoes and pings to the cross correlator 108.The cross correlator 108 performs multiple cross correlation operationson the filtered versions of the echoes and pings, and provides multiplesets of cross correlation output data to the data analyzer 110.

Next, the data analyzer 110 effectively constructs an ambiguity functionfor the echoes and corresponding transmitted pings. For example, FIG. 4a depicts representative ambiguity functions for multiple broadbandpings corresponding to frequency sub-band 31-43 kHz, FIG. 4 b depictsrepresentative ambiguity functions for multiple broadband pingscorresponding to frequency sub-band 56-68 kHz, and FIG. 4 c depictsrepresentative ambiguity functions for multiple broadband pingscorresponding to frequency sub-band 81-93 kHz. In each of the ambiguityfunctions depicted in FIGS. 4 a-4 c, a black dot is employed to indicatean estimated location of a cross correlation peak, as computed by thedata analyzer 110. Accordingly, the data analyzer 110 may use theestimated peak locations (as indicated by the black dots) to perform astatistical analysis for determining the level of peak ambiguity foreach ambiguity function.

As shown in FIGS. 4 a-4 c, the peak variability for each one of thepings having a center frequency equal to 37 kHz, 62 kHz, and 87 kHz,respectively, is indicated by the estimated locations of thecorresponding cross correlation peaks. For example, for the pingscorresponding to frequency sub-band 81-93 kHz (fc=87 kHz), the majorityof the computed peaks are located within the width of the main lobe ofthe ambiguity function when SNR=23 dB, thereby indicating low peakambiguity. However, when the SNR is successively reduced from 23 dB to18 dB, 13 dB, and 8 dB, the peak ambiguity gradually increases, asindicated by the increasing number of computed peaks located outside themain lobe (see FIG. 4 c). Similarly, for the pings corresponding tofrequency sub-band 56-68 kHz (fc=62 kHz), there is low peak ambiguitywhen SNR=23 dB, and increasing peak ambiguity as the SNR is successivelyreduced from 23 dB to 18 dB, 13 dB, and 8 dB (see FIG. 4 b); and, forthe pings corresponding to frequency sub-band 31-43 kHz (fc=37 kHz),there is low peak ambiguity when SNR=23 dB and 18 dB, and increasingpeak ambiguity when the SNR is successively reduced from 18 dB to 13 dBand 8 dB (see FIG. 4 c).

Accordingly, the technique of this second example may be employed toobtain an empirical estimation of the SNR in the environment ofinterest. For example, as shown in FIGS. 4 a-4 c, the lowest possibleSNR is empirically estimated to be about 18 dB. It is understood thatvarying levels of peak ambiguity and SNR may be obtained depending onthe center frequency and frequency range of the pings, and the specificcharacteristics of the sonar environment.

It should be understood that instead of transmitting multiple broadbandpings and band-pass filtering the resulting echoes, as in the secondexample above, multiple narrow-band pings corresponding to each of thefrequency sub-bands 6-18 kHz, 18-31 kHz, 31-43 kHz, 43-56 kHz, 56-68kHz, 68-81 kHz, and 81-93 kHz may alternatively be transmitted, therebyobviating the need for band-pass filtering. In both cases, the resultsgenerated by the data analyzer are essentially the same, i.e., manyestimates for each frequency sub-band, and therefore many distributionsfor the peak estimates. It is understood that the data analysis resultsobtained from transmitting the multiple broadband pings and the multiplenarrow-band pings may be combined for empirically estimating the lowestpossible SNR.

A first method of operating the presently disclosed system forestimating the SNR in a sonar environment is illustrated by reference toFIG. 5 a. As depicted in step 502, a sonar transmitter transmits asingle sonar pulse (“ping”) having a predetermined frequency rangethrough a transmission medium. Next, a sonar sensor/receiver receives,as depicted in step 504, a reflected sonar signal (“echo”) returningfrom a selected target. The receiver then provides, as depicted in step506, an indication of the echo to a plurality of band-pass filters,which pass respective frequency sub-bands spanning the predeterminedfrequency range. Next, the band-pass filters filter, as depicted in step508, the echo and a representation of the transmitted ping. A crosscorrelator then performs multiple cross correlation operations on thefiltered versions of the echo and pulse, as depicted in step 510, andprovides multiple sets of cross correlation output data corresponding tothe frequency sub-bands to a data analyzer. Next, the data analyzerdetermines, as depicted in step 512, the variability of crosscorrelation peaks within each one of the frequency sub-bands, andidentifies, as depicted in step 514, the lowest frequency sub-bandhaving a corresponding low peak ambiguity. The data analyzer thendetermines, as depicted in step 516, the break point on the peakvariability versus SNR curve corresponding to the identified frequencysub-band in this sonar environment where the sharp transition from lowerror levels to high error levels begins. Finally, the data analyzerestimates, as depicted in step 518, the SNR in the sonar environment tobe the SNR corresponding to the break point determined in step 516.

A second method of operating the presently disclosed system forestimating the SNR in a sonar environment is illustrated by reference toFIG. 5 b. As depicted in step 522, a sonar transmitter transmitsmultiple broadband pings having a predetermined frequency range througha transmission medium. Next, a sonar sensor/receiver receives, asdepicted in step 524, reflected sonar signals (echoes) returning fromone or more selected targets. The receiver then provides, as depicted instep 526, indications of the echoes to a plurality of band-pass filters,which pass respective frequency sub-bands having respective centerfrequencies. Next, the band-pass filters filter, as depicted in step528, the echoes and representations of the transmitted pings. A crosscorrelator then performs multiple cross correlation operations on thefiltered versions of the echoes and pings, as depicted in step 530, andprovides multiple sets of cross correlation output data corresponding tothe respective center frequencies to a data analyzer. Next, the dataanalyzer computes, as depicted in step 532, a cross correlation peak foreach ping, and determines, as depicted in step 534, the variability ofcross correlation peaks within each sub-band by a statistical analysisof the peak locations. The data analyzer then determines, as depicted instep 536, the center frequency that allows the lowest SNR whilemaintaining a low peak ambiguity, using the results of the statisticalanalysis of step 534. Finally, the data analyzer estimates, as depictedin step 538, the SNR in the sonar environment to be the lowest allowedSNR, as determined in step 536.

Having described the above illustrative embodiments, other alternativeembodiments or variations may be made. For example, when thecharacteristics of the transmitted pulse are unknown, e.g., in the caseof a passive sonar system, the transmitted pulse may be estimated byaveraging using multiple echoes or returns. Such averaging may take intoaccount some distortions (known or estimated) to the pulse. Theestimated signal may then be applied to the cross correlator, asdescribed above.

In addition, it was described in the second example above that multipleechoes and pings are provided to a plurality of band-pass filters forfiltering before performing cross correlation operations on the echoesand pings. However, in an alternative embodiment, the plurality offilters may be omitted, and unfiltered representations of the echoes andpings may be provided directly to the cross correlator.

In addition, it was described that for pulses having a fixed centralizedroot mean square bandwidth B_(CRMS) and different center frequencies fc,the behavior of the simulation curves 301-307 within region I (see FIGS.3 a-3 b) is characteristic of the performance of a coherent receiver,and the behavior of the curves 301-307 following their sharp transitionsfrom low error levels to high error levels within region II (see FIGS. 3a-3 b) is characteristic of the performance of a semi-coherent receiver.However, it should be understood that simulation curves similar to thecurves 301-307 may be generated to determine the root mean square error(RMSE, seconds) as a function of SNR (dB) and centralized root meansquare bandwidth B_(CRMS) for a fixed center frequency fc.

In this case, the behavior of the simulation curves would becharacteristic of the performance of a coherent receiver for high SNRlevels, and as the SNR level decreases, the pulses with lower B_(CRMS)values would be affected by peak ambiguity first, thereby causing theirperformance to degrade to that of a semi-coherent receiver. In general,the pulses with the larger B_(CRMS) values are more resilient to peakambiguity. As shown in FIGS. 3 a-3 b, as SNR levels decrease, the pulseswith higher center frequencies fc are affected by peak ambiguity first,and the pulses with lower center frequencies fc are more resilient topeak ambiguity. In a practical sonar system, however, only a limitedrange of frequencies may be employed for the sonar pulses and the sonarreceiver. This limitation places bounds on the pulses' center frequencyfc and centralized root mean square bandwidth B_(CRMS).

The effect of this limited frequency range within the practical sonarsystem is illustrated by the performance curve 308 (see FIG. 3 b).Specifically, the maximum allowable B_(CRMS) (e.g., 2.1 kHz) is the samefor all of the pulses used to generate the curve 308. Moreover, for highlevels of SNR (region I; see FIG. 3 b), the behavior of the performancecurve 308 is characteristic of a coherent receiver up to break point 9,which corresponds to the maximum allowable fc (e.g., 87 kHz). Thesemaximum allowable B_(CRMS) and fc values are a direct consequence of thelimited frequency range of the practical sonar system.

In addition, the sonar system 100 (see FIG. 1) may further include auser input device such as a keyboard or control panel to allow the userto configure the system. Further, the sonar sensor 102 may include atleast one suitable sonar transducer (e.g., a hydrophone sensor)operative to detect an echo signal, and the sonar receiver 104 mayinclude conventional filters and amplifiers for enhancing the echosignal before providing the signal to the plurality of band-pass filters106. The sonar system 100 may also include one or more computersoperative to store and process the sonar signal data and the crosscorrelation output data. For example, the data analyzer 110 may includeone or more microprocessors, application specific integrated circuits(ASICs), and/or microcomputers operative to analyze the signal andoutput data, in accordance with the methods disclosed herein. Inaddition, it is appreciated that the presently disclosed system forestimating SNR in noisy environments may be implemented for simulationand/or operation using one or more programmed general purpose computersand/or special purpose hardware.

It will be further appreciated by those of ordinary skill in the artthat modifications to and variations of the above-described estimationof background noise and its effect on sonar range estimation may be madewithout departing from the inventive concepts disclosed herein.Accordingly, the invention should not be viewed as limited except as bythe scope and spirit of the appended claims.

1. A system for estimating the instantaneous signal-to-noise ratio (SNR)in an environment, comprising: a transmitter configured to transmit atleast one first signal through a predetermined transmission mediumwithin the environment, the first signal having a predeterminedfrequency range, wherein the first signal travels tbrough thetransmission medium until it strikes at least one object, therebygenerating at least one second signal reflected from the object; aplurality of band-pass filters, each band-pass filter being configuredto pass a respective sub-band of frequencies, each band-pass filterbeing further configured to receive representations of the first andsecond signals, to filter the representations of the first and secondsignals, and to provide filtered versions of the first and secondsignals; a cross correlator configured to receive the filtered versionsof the first and second signals provided by the respective band-passfilters, and to perform multiple cross correlation operations on thefiltered first and second signals, thereby providing cross correlationoutput data, wherein each cross correlation operation operates on thefiltered first and second signals provided by a respective one of theband-pass filters; and a data analyzer contiqured to receive the crosscorrelation output data, and to analyze the output data for determiningvariability of cross correlation peaks within each frequency sub-band,for identifying the lowest frequency sub-band having a corresponding lowpeak ambiguity, and for estimating the SNR in the environment based onthe peak variability and center frequency of the identified frequencysub-band and the predetermined frequency range.
 2. The system of claim 1further including a sensor configured to receive the at least one secondsignal.
 3. The system of claim 2 wherein the sensor comprises at leastone hydrophone sensor.
 4. The system of claim 2 further including areceiver configured to receive an indication of the second signal fromthe sensor, and to provide the representation of the second signal tothe plurality of band-pass filters.
 5. The system of claim 1 wherein thetransmitter is configured to transmit a plurality of first signalsthrough the transmission medium, each first signal having thepredetermined frequency range, wherein the plurality of first signalstravel through the transmission medium until they strike at least oneobject, thereby generating a plurality of second signals reflected fromthe object.
 6. The system of claim 5 wherein the data analyzer isfurther configured to analyze the cross correlation output data fordetermining a plurality of cross correlation peak locations relative torespective ambiguity functions corresponding to the frequency sub-bands.7. The system of claim 6 wherein the data analyzer is further configuredto perform a statistical analysis on the plurality of peak locations fordetermining the variability of cross correlation peaks within eachfrequency sub-band.
 8. The system of claim 5 wherein each first signalcomprises a sonar ping.
 9. The system of claim 1 wherein the respectivefrequency sub-bands are contiguous and substantially span thepredetermined frequency range of the first signal.
 10. The system ofclaim 1 wherein the predetermined frequency range is a maximumcentralized root mean square bandwidth of the first signal.
 11. Thesystem of claim 1 wherein the system operates as a coherent receiver forsignal frequencies ranging from a maximum frequency through theidentified frequency sub-band.
 12. The system of claim 1 wherein thesystem operates as a semi-coherent receiver for signal frequenciesranging from the identified frequency sub-band to a minimum frequency.13. The system of claim 1 wherein the predetermined transmission mediumis one of air, water, soil, and living tissue.
 14. A method ofestimating the instantaneous signal-to-noise ratio (SNR) in anenvironment, comprising the steps of: transmitting at least one firstsignal through a predetermined transmission medium within theenvironment by a transmitter, the first signal having a predeterminedfrequency range, wherein the first signal travels through thetransmission medium until it strikes at least one object, therebygenerating at least one second signal reflected from the object;receiving representations of the first and second signals by a pluralityof band-pass filters, each band-pass filter being configured to pass arespective sub-band of frequencies; filtering the representations of thefirst and second signals by each band-pass filter; receiving thefiltered versions of the first and second signals by a cross correlator;performing multiple cross correlation operations on the filtered firstand second signals by the cross correlator, thereby providing crosscorrelation output data, wherein each cross correlation operationoperates on the filtered first and second signals provided by arespective one of the band-pass filters; receiving the cross correlationoutput data by a data analyzer; determining variability of crosscorrelation peaks within each frequency sub-band by the data analyzer;identifying the lowest frequency sub-band having a corresponding lowpeak ambiguity by the data analyzer; and estimating the SNR in theenvironment based on the peak variability and center frequency of theidentified frequency sub-band and the predetermined frequency range bythe data analyzer.
 15. The method of claim 14 further including the stepof receiving the at least one second signal by a sensor.
 16. The methodof claim 15 wherein the sensor comprises at least one hydrophone sensor.17. The method of claim 15 further including the steps of receiving anindication of the second signal from the sensor by a receiver, andproviding the representation of the second signal to the plurality ofband-pass filters by the receiver.
 18. The method of claim 14 furtherincluding the steps of transmitting a plurality of first signals throughthe transmission medium by the transmitter, each first signal having thepredetermined frequency range, wherein the plurality of first signalstravel through the transmission medium until they strike at least oneobject, thereby generating a plurality of second signals reflected fromthe object.
 19. The method of claim 18 further including the step ofanalyzing the cross correlation output data by the data analyzer fordetermining a plurality of cross correlation peak locations relative torespective ambiguity functions corresponding to the frequency sub-bands.20. The method of claim 19 further including the step of performing astatistical analysis of the plurality of peak locations by the dataanalyzer for determining the variability of cross correlation peakswithin each frequency sub-band.
 21. The method of claim 18 wherein eachfirst signal comprises a sonar ping.
 22. The method of claim 14 whereinthe respective frequency sub-bands are contiguous and substantially spanthe predetermined frequency range of the first signal.
 23. The method ofclaim 14 wherein the predetermined frequency range is a maximumcentralized root mean square bandwidth of the first signal.
 24. Themethod of claim 14 further including the step of operating as a coherentreceiver for signal frequencies ranging from a maximum frequency throughthe identified frequency sub-band.
 25. The method of claim 14 furtherincluding the step of operating as a semi-coherent receiver for signalfrequencies ranging from the identified frequency sub-band to a minimumfrequency.
 26. The method of claim 14 wherein the predeterminedtransmission medium is one of air, water, soil, and living tissue.
 27. Asystem for estimating the instantaneous signal-to-noise ratio (SNR) inan environment, comprising: a transmitter configured to transmit aplurality of first signals through a transmission medium, the pluralityof first signals spanning respective frequency sub-bands, wherein theplurality of first signals travel through the transmission medium untilthey strike at least one object, thereby generating a plurality ofsecond signals reflected from the object; a cross correlator configuredto receive the first and second signals and to perform multiple crosscorrelation operations on the first and second signals, therebyproviding cross correlation output data, wherein each cross correlationoperation operates on a first and second signal pair corresponding to arespective frequency sub-band; and a data analyzer configured to receivethe cross correlation output data, and to analyze the output data fordetermining variability of cross correlation peaks within each frequencysub-band, for identifying the lowest frequency sub-band having acorresponding low peak ambiguity, and for estimating the SNR in theenvironment based on the peak variability and center frequency of theidentified frequency sub-band.
 28. The system of claim 27 wherein thedata analyzer is further configured to analyze the cross correlationoutput data for determining a plurality of cross correlation peaklocations relative to respective ambiguity functions corresponding tothe frequency sub-bands.
 29. The system of claim 28 wherein the dataanalyzer is further configured to perform a statistical analysis on theplurality of peak locations for determining the variability of crosscorrelation peaks within each frequency sub-band.
 30. The system ofclaim 27 wherein each first signal comprises a sonar ping.
 31. A methodof estimating the instantaneous signal-to-noise ratio (SNR) in anenvironment, comprising the steps of: transmitting a plurality of firstsignals through a transmission medium by a transmitter, the plurality offirst signals spanning respective frequency sub-bands, wherein theplurality of first signals travel through the transmission medium untilthey strike at least one object, thereby generating a plurality ofsecond signals reflected from the object; receiving the first and secondsignals by a cross correlator; performing multiple cross correlationoperations on the first and second signals by the cross correlator,thereby providing cross correlation output data, wherein each crosscorrelation operation operates on a first and second signal paircorresponding to a respective frequency sub-band; receiving the crosscorrelation output data by a data analyzer; and analyzing the outputdata for determining variability of cross correlation peaks within eachfrequency sub-band by the data analyzer, thereby identifying the lowestfrequency sub-band having a corresponding low peak ambiguity andestimating the SNR in the environment based on the peak variability andcenter frequency of the identified frequency sub-band.
 32. The method ofclaim 31 wherein the analyzing step includes analyzing the crosscorrelation output data for determining a plurality of cross correlationpeak locations relative to respective ambiguity functions correspondingto the frequency sub-bands.
 33. The method of claim 32 wherein theanalyzing step further includes performing a statistical analysis on theplurality of peak locations for determining the variability of crosscorrelation peaks within each frequency sub-band.